An Agile Approach to Collaborative Online International Learning: A Case Study of Virtual Indigenous Food Sovereignty and Public Policy Internships in Aotearoa New Zealand and Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Covid-19 and the policy shift to lockdowns had a considerable impact on global higher education. Campuses transitioned to virtual, online teaching leveraging a host of learning technologies to deliver educational content. While many universities had existing infrastructure to shift to online content delivery, interactive, collaborative learning within this virtual teaching space was not as simple. Students were unable to travel for valuable exchange and field-based learning activities, including applied research and internship opportunities. This article considers one attempt during Covid-19 lockdowns in Aotearoa New Zealand and Ontario, Canada to co-create and deliver an innovative, cross-national virtual learning environment. The project that emerged from theseunprecedented circumstances asked: how can students in different countries, on opposite sides of the globe, engage in virtual collaborations to develop practical insights into global, locally relevant public policy problems? The model leveraged existing academic staff, university resources and existing relationships between researchers and community organizations to provide a successful model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it